Towards precision medicine: Omics approach for COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic had a devastating impact on human society. Beginning with genome surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the development of omics technologies brought a clearer understanding of the complex SARS-CoV-2 and COVID-19. Here, we reviewed how omics, including genomics, proteomics, single-cell multi-omics, and clinical phenomics, play roles in answering biological and clinical questions about COVID-19. Large-scale sequencing and advanced analysis methods facilitate COVID-19 discovery from virus evolution and severity risk prediction to potential treatment identification. Omics would indicate precise and globalized prevention and medicine for the COVID-19 pandemic under the utilization of big data capability and phenotypes refinement. Furthermore, decoding the evolution rule of SARS-CoV-2 by deep learning models is promising to forecast new variants and achieve more precise data to predict future pandemics and prevent them on time.

Metabolic profile of women with premature ovarian insufficiency compared with that of age-matched healthy controls.

OBJECTIVE: . To compare the metabolic profile of women with spontaneous premature ovarian insufficiency (POI) with that of age-matched healthy controls. STUDY DESIGN: . A cross-sectional case-control study was conducted using 1:1 matching by age. Women below the age of 40 with spontaneous POI who did not receive any medication (n = 303) and age-matched healthy women (n = 303) were included in this study. MAIN OUTCOME MEASURES: . Metabolic profiles, including serum levels of total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), glucose, uric acid, urea and creatinine, were compared between women with POI and controls. For women with POI, factors associated with the metabolic profile were analyzed. RESULTS: . Women with POI were more likely to exhibit increased serum levels of TG (ß, 0.155; 95% CI, 0.086, 0.223) and glucose (0.067; 0.052, 0.083), decreased levels of HDL-C (-0.087; -0.123, -0.051), LDL-C (-0.047; -0.091, -0.003) and uric acid (-0.053; -0.090, -0.015), and impaired kidney function (urea [0.070; 0.033, 0.107]; creatinine [0.277; 0.256, 0.299]; eGFR [-0.234; -0.252, -0.216]) compared with controls after adjusting for age and BMI. BMI, parity, gravidity, FSH and E2 levels were independent factors associated with the metabolic profile of women with POI. CONCLUSION: . Women with POI exhibited abnormalities in lipid metabolism, glucose metabolism, and a decrease in kidney function. In women with POI, early detection and lifelong management of metabolic abnormalities are needed.

Prediction of Microvascular Invasion of Hepatocellular Carcinoma Based on Contrast-Enhanced MR and 3D Convolutional Neural Networks.

BACKGROUND AND PURPOSE: It is extremely important to predict the microvascular invasion (MVI) of hepatocellular carcinoma (HCC) before surgery, which is a key predictor of recurrence and helps determine the treatment strategy before liver resection or liver transplantation. In this study, we demonstrate that a deep learning approach based on contrast-enhanced MR and 3D convolutional neural networks (CNN) can be applied to better predict MVI in HCC patients. MATERIALS AND METHODS: This retrospective study included 114 consecutive patients who were surgically resected from October 2012 to October 2018 with 117 histologically confirmed HCC. MR sequences including 3.0T/LAVA (liver acquisition with volume acceleration) and 3.0T/e-THRIVE (enhanced T1 high resolution isotropic volume excitation) were used in image acquisition of each patient. First, numerous 3D patches were separately extracted from the region of each lesion for data augmentation. Then, 3D CNN was utilized to extract the discriminant deep features of HCC from contrast-enhanced MR separately. Furthermore, loss function for deep supervision was designed to integrate deep features from multiple phases of contrast-enhanced MR. The dataset was divided into two parts, in which 77 HCCs were used as the training set, while the remaining 40 HCCs were used for independent testing. Receiver operating characteristic curve (ROC) analysis was adopted to assess the performance of MVI prediction. The output probability of the model was assessed by the independent student's t-test or Mann-Whitney U test. RESULTS: The mean AUC values of MVI prediction of HCC were 0.793 (p=0.001) in the pre-contrast phase, 0.855 (p=0.000) in arterial phase, and 0.817 (p=0.000) in the portal vein phase. Simple concatenation of deep features using 3D CNN derived from all the three phases improved the performance with the AUC value of 0.906 (p=0.000). By comparison, the proposed deep learning model with deep supervision loss function produced the best results with the AUC value of 0.926 (p=0.000). CONCLUSION: A deep learning framework based on 3D CNN and deeply supervised net with contrast-enhanced MR could be effective for MVI prediction.

Prediction of Microvascular Invasion of Hepatocellular Carcinoma Based on Preoperative Diffusion-Weighted MR Using Deep Learning.

RATIONALE AND OBJECTIVES: To investigate the value of diffusion-weighted magnetic resonance imaging for the prediction of microvascular invasion (MVI) of Hepatocellular Carcinoma (HCC) using Convolutional Neural Networks (CNN). MATERIAL AND METHODS: This study was approved by the local institutional review board and the patients' informed consent was waived. Consecutive 97 subjects with 100 HCCs from July 2012 to October 2018 with surgical resection were retrieved. All subjects with diffusion-weighted imaging (DWI) examinations were performed with single-shot echo-planar imaging in a breath-hold routine. DWI parameters were three b values of 0,100,600 sec/mm2. First, apparent diffusion coefficients (ADC) images were computed by mono-exponentially fitting the three b-value points. Then, multiple 2D axial patches (28 × 28) of HCCs from b0, b100, b600, and ADC images were extracted to increase the dataset for training the CNN model. Finally, the fusion of deep features derived from three b value images and ADC was conducted based on the CNN model for MVI prediction. The data set was split into the training set (60 HCCs) and the independent test set (40 HCCs). The output probability of the deep learning model in the MVI prediction of HCCs was assessed by the independent student's t-test for data following a normal distribution and Mann-Whitney U test for data violating the normal distribution. Receiver operating characteristic curve and area under the curve (AUC) were also used to assess the performance for MVI prediction of HCCs in the fixed test set. RESULTS: Deep features in b600 images yielded better performance (AUC = 0.74, p = 0.004) for MVI prediction than b0 (AUC = 0.69, p = 0.023) and b100 (AUC = 0.734, p = 0.011). Comparatively, deep features in the ADC map obtained lower performance (AUC = 0.71, p = 0.012) than that of the higher b value images (b600) for MVI prediction. Furthermore, the fusion of deep features from the b0, b100, b600, and ADC images yielded the best results (AUC = 0.79, p = 0.002) for MVI prediction. CONCLUSION: Fusion of deep features derived from DWI images concerning the three b-value images and the ADC image yields better performance for MVI prediction.

Improving the malignancy characterization of hepatocellular carcinoma using deeply supervised cross modal transfer learning for non-enhanced MR.

The malignancy characterization of hepatocellular carcinoma (HCC) is remarkably significant in clinical practice. In this work, we propose a deeply supervised cross modal transfer learning method to remarkably improve the malignancy characterization of HCC based on non-enhanced MR. First, we use samples of non-enhanced and contrast-enhanced MR for pre-training a deep learning network to learn the cross modal relationship between the non-enhanced modal and enhanced modal. Then, the parameters of the pre-trained across modal representation are transferred to a second deep learning model for fine-tuning based only on non-enhanced MR for malignancy characterization of HCC. Specifically, a deeply supervised network is designed to enhance the stability of the second deep learning model and further improve the performance of lesion characterization. Importantly, only non-enhanced MR of HCC is required for the malignancy characterization in the training and test phase of the second deep learning model. Experiments of one hundred and fifteen clinical HCCs demonstrate that the proposed deeply supervised cross modal transfer learning method can significantly improve the malignancy characterization of HCC based on non-enhanced MR.